For the resulting 8 experimental combinations the nMAE varied between nMAE = 0.16 and nMAE = 0.21 (lower numbers better). An additional quality score (QS) was introduced that allows direct comparison between different movements. This score ranged from QS = 0.25 to QS = 0.40 (higher numbers better) for the experimental combinations. The above formulated aim was achieved with good prediction quality by using only 8 individual measurements (easy to collect body dimensions) and the subsequent optimization of only 5 parameters. At the same time, just easily accessible sEMG measurement locations are used to enable simple integration, e.g. in exoskeletons.
This biomechanical model does not compete with models that measure all sEMG signals of the muscle heads involved in order to achieve the highest possible prediction quality, but the quality of the movement prediction of the domain model in this work will serve as a reference for future work.
Future work includes the further development of the domain model shown here towards a hybrid model that integrates additional, data-driven (ML-based) sub-models. Such a hybrid approach could, on the one hand, further increase the prediction quality and, on the other hand, identify and address over-simplifications in sub-models of the existing domain model. Such a hybrid approach should also combine the explainability of a pure domain model with the adaptability of a purely data-driven (black box) approach.
Grimmelsmann N, Mechtenberg M,Schenck W, Meyer HG, Schneider A (2023): sEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters.
PLoS ONE 18(8): e0289549.